CN117269660A - Fault arc detection method and system based on variation coefficient difference algorithm - Google Patents
Fault arc detection method and system based on variation coefficient difference algorithm Download PDFInfo
- Publication number
- CN117269660A CN117269660A CN202310213243.2A CN202310213243A CN117269660A CN 117269660 A CN117269660 A CN 117269660A CN 202310213243 A CN202310213243 A CN 202310213243A CN 117269660 A CN117269660 A CN 117269660A
- Authority
- CN
- China
- Prior art keywords
- coefficient
- fault
- variation
- noise reduction
- threshold
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000001514 detection method Methods 0.000 title claims abstract description 68
- 230000009467 reduction Effects 0.000 claims abstract description 55
- 238000000354 decomposition reaction Methods 0.000 claims abstract description 39
- 230000003044 adaptive effect Effects 0.000 claims abstract description 35
- 230000006978 adaptation Effects 0.000 claims description 24
- 238000012545 processing Methods 0.000 claims description 13
- 238000012937 correction Methods 0.000 claims description 10
- 238000000605 extraction Methods 0.000 claims description 6
- 238000000034 method Methods 0.000 abstract description 11
- 238000004364 calculation method Methods 0.000 description 7
- 238000010586 diagram Methods 0.000 description 4
- 239000000284 extract Substances 0.000 description 4
- 238000012512 characterization method Methods 0.000 description 3
- 238000002474 experimental method Methods 0.000 description 3
- 238000012360 testing method Methods 0.000 description 3
- 230000009466 transformation Effects 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 2
- 239000000463 material Substances 0.000 description 2
- 238000005070 sampling Methods 0.000 description 2
- 206010000369 Accident Diseases 0.000 description 1
- 230000032683 aging Effects 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 230000015556 catabolic process Effects 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 238000004880 explosion Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 230000000737 periodic effect Effects 0.000 description 1
- 238000007781 pre-processing Methods 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 230000004044 response Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/08—Locating faults in cables, transmission lines, or networks
- G01R31/081—Locating faults in cables, transmission lines, or networks according to type of conductors
- G01R31/085—Locating faults in cables, transmission lines, or networks according to type of conductors in power transmission or distribution lines, e.g. overhead
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/08—Locating faults in cables, transmission lines, or networks
- G01R31/088—Aspects of digital computing
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/12—Testing dielectric strength or breakdown voltage ; Testing or monitoring effectiveness or level of insulation, e.g. of a cable or of an apparatus, for example using partial discharge measurements; Electrostatic testing
- G01R31/1227—Testing dielectric strength or breakdown voltage ; Testing or monitoring effectiveness or level of insulation, e.g. of a cable or of an apparatus, for example using partial discharge measurements; Electrostatic testing of components, parts or materials
- G01R31/1263—Testing dielectric strength or breakdown voltage ; Testing or monitoring effectiveness or level of insulation, e.g. of a cable or of an apparatus, for example using partial discharge measurements; Electrostatic testing of components, parts or materials of solid or fluid materials, e.g. insulation films, bulk material; of semiconductors or LV electronic components or parts; of cable, line or wire insulation
- G01R31/1272—Testing dielectric strength or breakdown voltage ; Testing or monitoring effectiveness or level of insulation, e.g. of a cable or of an apparatus, for example using partial discharge measurements; Electrostatic testing of components, parts or materials of solid or fluid materials, e.g. insulation films, bulk material; of semiconductors or LV electronic components or parts; of cable, line or wire insulation of cable, line or wire insulation, e.g. using partial discharge measurements
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Mathematical Physics (AREA)
- Theoretical Computer Science (AREA)
- Testing Relating To Insulation (AREA)
Abstract
The invention discloses a fault arc detection method and a fault arc detection system based on a variation coefficient differential algorithm, wherein the method comprises the steps of collecting current signals of a line to be detected; obtaining a second-order difference value sample sequence of the variation coefficient according to the current signal; performing wavelet decomposition on the second-order differential value sample sequence, and performing noise reduction treatment on the decomposition result to obtain a noise reduction coefficient; calculating an adaptive threshold based on the post-noise reduction coefficient; and detecting fault arc based on the adaptive threshold. The invention overcomes the problems of low accuracy, missing report and more false reports caused by the fact that the traditional algorithm cannot adapt to the threshold value in real time in fault detection, thereby improving the reliability of detection.
Description
Technical Field
The invention relates to the technical field of arc detection, in particular to a fault arc detection method and system based on a variation coefficient differential algorithm.
Background
The fault arc caused by the factors such as damage, aging, connection looseness and the like of the power distribution system circuit can cause local high temperature, and is extremely easy to cause electric fire and even explosion, so the fault arc is one of the most main causes of the electric fire. The center temperature of the arc can be as high as 5000K to 15000K, and the arc occurs frequently once a breakdown point occurs. Therefore, the fire accident caused by the electrical fire is generally serious, and it is a significant matter to avoid the occurrence of the electrical fire as much as possible.
In order to prevent fire caused by fault arcs, fault arc detection technology is generally adopted to detect fault arcs, and most fault arc detectors in the market currently adopt fault arc detection methods based on eigenvector threshold monitoring. However, the characteristic threshold set by the detector is generally a fixed threshold, and when the characteristic threshold is set to be too high, the condition of detection failure can occur when arc fault detection is performed; if the characteristic threshold value is set too low, false alarm occurs. It is obviously difficult and important to set a proper characteristic threshold for various types of loads in an actual power utilization scene, so the characteristic threshold set by the fault arc detector is quite proper and important if an adaptive threshold strategy is adopted.
In the related art, a combined fault detection method based on wavelet transformation and Singular Value Decomposition (SVD) is proposed in patent application publication No. CN109061414a to detect a fault arc signal; the scheme is suitable for a direct current system, and wavelet coefficients are analyzed from the singular value angle and are inconsistent with the expression form of an alternating current system.
Based on the statistical information of the virtual energy index, the patent application document with publication number CN112162172A establishes a periodic fluctuation index and a dynamic threshold value, and a fault judging method and a dynamic threshold updating algorithm; the scheme is based on virtual energy indexes established by indexes such as maximum value, minimum value, mean value, variance and the like.
Disclosure of Invention
The technical problem to be solved by the invention is how to improve the reliability of fault arc detection.
The invention solves the technical problems by the following technical means:
in a first aspect, the present invention provides a fault arc detection method based on a coefficient of variation differential algorithm, where the method includes:
collecting a current signal of a line to be tested;
obtaining a second-order difference value sample sequence of the variation coefficient according to the current signal;
performing wavelet decomposition on the second-order differential value sample sequence, and performing noise reduction treatment on the decomposition result to obtain a noise reduction coefficient;
calculating an adaptive threshold based on the post-noise reduction coefficient;
and detecting fault arc based on the adaptive threshold.
Further, the obtaining a sample sequence of the second order difference value of the variation coefficient according to the current signal includes:
extracting a variation coefficient of each half period of the current signal;
calculating a first-order differential value of the current half period based on the variation coefficient of the current half period and the variation coefficient of the next half period;
calculating a second-order differential value of the current half period based on the first-order differential value of the current half period and the first-order differential value of the next half period;
the second-order differential value of each half period is formed into the second-order differential value sample sequence.
Further, the performing wavelet decomposition on the second-order differential value sample sequence, and performing noise reduction processing on the decomposition result to obtain a noise reduction coefficient, including:
performing wavelet decomposition on the second-order differential value sample sequence to obtain an approximation coefficient and a high-frequency detail coefficient;
and carrying out hard threshold wavelet noise reduction processing on the high-frequency detail coefficient to obtain a noise-reduced coefficient.
Further, the performing hard threshold wavelet noise reduction processing on the high-frequency detail coefficient to obtain a noise-reduced coefficient includes:
when the absolute value of the high-frequency detail coefficient is larger than a given universal threshold lambda, the coefficient of the high-frequency detail coefficient is unchanged;
setting zero for the coefficient of the high-frequency detail coefficient when the absolute value of the high-frequency detail coefficient is smaller than or equal to a given universal threshold lambda;
wherein the universal threshold valueSigma is the standard deviation of the coefficients of the detail components, n D Is the number of coefficients of the detail component.
Further, the calculating an adaptive threshold based on the post-noise reduction coefficient includes:
performing wavelet reconstruction on the coefficient after noise reduction to obtain a section of discrete sequence with the same length as the current signal;
taking the information entropy of the discrete sequence as an arc fault factor;
calculating a load adaptation factor based on the coefficient reconstructed from the noise-reduced coefficient;
and calculating the self-adaptive threshold according to the arc fault factor, the load adaptation factor and the empirical self-correction factor.
Further, the arc fault factor is formulated as:
wherein: the AFF is the arc fault factor and,represents the i +.>Values of the sequence, n D Representation->Number of coefficients in the sequence,/->Representing the offline sequence.
Further, the load adaptation factor is formulated as:
wherein: LAF is the load adaptation factor, d i And (3) denoising the third layer detail coefficients of the current signal and reconstructing the coefficients, wherein i=1, 2, … and N.
Further, the adaptive threshold is formulated as:
thd＝k·AFF·LAF
wherein: thd is an adaptive threshold, AFF is an arc fault factor, LAF is a load adaptation factor, and k is an empirical self-correction factor.
Further, the fault arc detection based on the adaptive threshold includes:
when the real-time threshold t of one detection period is larger than or equal to the self-adaptive threshold, determining that a series fault arc occurs;
when the real-time threshold t of one detection period is smaller than the adaptive threshold, determining that no series fault arc occurs.
In a second aspect, the present invention further provides a fault arc detection system based on a coefficient of variation differential algorithm, where the system includes:
the acquisition module is used for acquiring current signals of the line to be detected;
the characteristic extraction module is used for obtaining a second-order difference value sample sequence of the variation coefficient according to the current signal;
the decomposition noise reduction module is used for carrying out wavelet decomposition on the second-order differential value sample sequence and carrying out noise reduction treatment on the decomposition result to obtain a noise reduction coefficient;
the threshold calculating module is used for calculating an adaptive threshold based on the noise reduction coefficient;
and the detection module is used for detecting fault arcs based on the self-adaptive threshold value.
The invention has the advantages that:
(1) The invention extracts a variation coefficient second-order differential value sample sequence of a current signal as a fault characteristic value, carries out wavelet decomposition and noise reduction treatment on the second-order differential value sample sequence to obtain a noise-reduced coefficient, thereby determining a self-adaptive threshold value according to the noise-reduced coefficient, and finally detecting a fault arc by utilizing the self-adaptive threshold value; the invention adopts the technical means of combining the variation coefficient difference algorithm, the self-adaptive threshold value, the wavelet decomposition reconstruction and the noise reduction, overcomes the problems of low accuracy, missing report and more false reports caused by the fact that the traditional algorithm cannot adapt to the threshold value in real time in fault detection, and further improves the reliability of detection.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
Drawings
Fig. 1 is a schematic flow chart of a fault arc detection method based on a coefficient of variation difference algorithm according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of wavelet decomposition principle in an embodiment of the present invention;
FIG. 3 is a schematic view of wavelet reconstruction principle in an embodiment of the present invention;
FIG. 4 is a graph of the wavelet transform detection of resistive load current according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of the current signal and sequence of a resistive load under normal and arc conditions, as shown in an embodiment of the present invention;
FIG. 6 is an arc fault factor AFF of a current signal of a resistive load under normal and arc conditions, shown in an embodiment of the present invention;
FIG. 7 is a load adaptation factor LAF of a current signal of a resistive load under normal and arcing conditions, as shown in an embodiment of the present invention;
FIG. 8 is an adaptive threshold thd of the current signal of a resistive load under normal and arcing conditions, as shown in an embodiment of the present invention;
FIG. 9 is a schematic diagram of a test platform according to an embodiment of the present invention;
fig. 10 is a schematic structural diagram of a fault arc detection system based on a coefficient of variation difference algorithm according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions in the embodiments of the present invention will be clearly and completely described in the following in conjunction with the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
As shown in fig. 1, a first embodiment of the present invention proposes a fault arc detection method based on a coefficient of variation differential algorithm, the method comprising the steps of:
s10, collecting a current signal of a line to be tested;
after the device is initialized, when the real-time current signal of the device is obtained, the current signal in any load circuit can be obtained through an external circuit to serve as a signal to be measured.
S20, obtaining a second-order difference value sample sequence of the variation coefficient according to the current signal;
s30, carrying out wavelet decomposition on the second-order differential value sample sequence, and carrying out noise reduction treatment on the decomposition result to obtain a noise reduction coefficient;
s40, calculating an adaptive threshold value based on the noise reduction coefficient;
and S50, fault arc detection is carried out based on the self-adaptive threshold value.
Aiming at an alternating current system, the embodiment starts from the relation between the variability and the variability reflected by the wavelet coefficients and the fault arc, and the fault arc is represented by the variation coefficients, and the technical means of combining a variation coefficient difference algorithm, an adaptive threshold value, wavelet decomposition reconstruction and noise reduction are adopted, so that the problems of low accuracy, missing report and more false reports caused by the fact that the conventional algorithm cannot adapt to the threshold value in real time in fault detection are overcome, and the reliability of detection is further improved.
In one embodiment, the step S20: according to the current signal, a second order difference value sample sequence of a variation coefficient is obtained, and the method comprises the following steps:
s21, extracting a variation coefficient of each half period of the current signal;
specifically, assume that equally spaced sample values of a adjacent periods are detected, each period consisting of B sample points. Calculating the number of points per half period of the signal to be measuredThe formula is: />Wherein F is s For the sampling frequency, f is 50Hz.
Calculating the coefficient of variation of the mth half periodWherein (1)>For the standard deviation of the coefficients per half period +.>Taking the average value of the absolute values of the coefficients in each half period, wherein x (i) is a wavelet coefficient, and N is the total number of wavelet coefficients.
S22, calculating a first-order difference value of the current half period based on the variation coefficient of the current half period and the variation coefficient of the next half period;
specifically, the first-order difference value C.V of the coefficient of variation of the mth half cycle (1) (m)＝|C·V(m+1)-C·V(m)|。
S23, calculating a second-order differential value of the current half period based on the first-order differential value of the current half period and the first-order differential value of the next half period;
specifically, the second-order differential value C.V of the coefficient of variation of the mth half period (2) (m)＝|C·V (1) (m+1)-C·V (1) (m)|。
S24, forming a second-order differential value of each half period into a second-order differential value sample sequence by using D (2) And (3) representing.
The coefficient of variation Differential Algorithm (DACV) is a low-voltage series fault arc detection algorithm based on an arc electrical signal and a differential algorithm. In order to avoid potential damage to the source signal caused by preprocessing means such as noise reduction and the like, the purity of the source signal is maintained, the DACV selects to directly extract fault characteristics of the acquired source signal preferentially, and then signal processing technologies such as noise reduction and the like are utilized for data enhancement on subsequent processing. In this embodiment, the DACV needs to consider the magnitude of c·v and the first-order and second-order differential values thereof as criteria for determining whether a series fault arc occurs.
It should be noted that the Daubechies wavelet family has a large number of vanishing moments, and can extract local signal disturbance with high quality. By comparing the performance of various wavelet extraction fault arcs, the embodiment preferably uses the db4 wavelet which is tightly supported and orthogonal. In order to study the capability of wavelet transformation to represent a fault arc, the present embodiment performs the following experiment, as shown in fig. 4, in which the wavelet transformation detects a resistive load current, and d3 is a high-frequency detail signal coefficient obtained by reconstruction after wavelet decomposition. The principle processes of wavelet decomposition and reconstruction are shown in fig. 2 and 3, respectively.
It can be seen from fig. 4 that the place of the high-frequency detail coefficient d3 where there is a significant fluctuation corresponds exactly to the phase of occurrence of the fault arc. In other words, d3 is able to accurately catch the fault arc. Some high frequency detail coefficients when a fault arc occurs are significantly improved in value over normal conditions. It is expected that the fault indication features extracted based on the high frequency detail coefficient d3 should have a good characterization effect on the fault but still require further analysis.
Considering that the existing fault generating device and test platform in the market are difficult to perform frequent tests stably, the embodiment also establishes an experiment platform which accords with the U.S. standard UL 1699 and national standard GB14287.4, as shown in FIG. 9, and comprises a power supply, a circuit breaker, a current transformer, a data acquisition card, a PC, a fault arc generator and various experimental loads.
The data acquisition card and the current transformer are adopted to acquire related current data, the sampling frequency Fs is 50KHz, and the conditions of different types of loads are considered. The frequency band of the nth detail signal is (Fs/2n+1, fs/2 n), and correspondingly, the frequency band of the nth approximation signal is (0, fs/2n+1). Current samples were collected under normal and arcing conditions, saved on a PC and pre-processed and SAF detection strategies were implemented.
It should be noted that the current data under different working conditions (normal working and fault arc occurrence) and different loads are obtained through experiments of controlling whether the arc is generated or not and connecting different types of electric appliances by adjusting the clamping gap of the fault arc generator.
Further, the series fault arc condition adopted in this embodiment is that four peaks occur in two basic periods. The 2 basic periods correspond to 4 half periods, i.e. 4 C.V samples, 3 C.V (1) 2 C.V (2) . In order to adapt the conditions to the coefficient of variation difference algorithm described in the present application, four consecutive cycles of the original signal are input to obtain 6 C.V (2) Sample and assume that the fault condition corresponds to C. V, C.V occurring within 80ms (1) 、C·V (2) At least four peak events that occur in combination.
Further, based on the current signal and sequence C.V, C.V of resistive load under normal and arc conditions of resistive load of the present application (1) ，C·V (2) As shown in fig. 5.
In one embodiment, S30, performing wavelet decomposition on the second-order differential value sample sequence, and performing noise reduction processing on the decomposition result to obtain a noise-reduced coefficient, where the method includes the following steps:
s31, carrying out wavelet decomposition on the second-order differential value sample sequence to obtain an approximation coefficient and a high-frequency detail coefficient;
s32, performing hard threshold wavelet noise reduction processing on the high-frequency detail coefficient to obtain a noise-reduced coefficient.
It should be noted that if the wavelet decomposition is directly performed on the current signal, a part of details will be lost, which is unfavorable for extracting the variation coefficient of each half period, because the noise and the fault arc are mostly represented on the high-frequency detail coefficient, if the environmental noise is negligible, the embodiment preferably directly extracts the variation coefficient of each half period for the sample sequence
In one embodiment, the step S32: the hard threshold wavelet noise reduction processing is carried out on the high-frequency detail coefficient to obtain a noise-reduced coefficient, and the method comprises the following steps:
s321, when the absolute value of the high-frequency detail coefficient is larger than a given universal threshold lambda, the coefficient of the high-frequency detail coefficient is unchanged;
s322, when the absolute value of the high-frequency detail coefficient is smaller than or equal to a given universal threshold lambda, setting the coefficient of the high-frequency detail coefficient to zero;
wherein the universal threshold valueSigma is the standard deviation of the coefficients of the detail components, n D Is the number of coefficients of the detail component.
Further, hard threshold function:
where x (i) represents a wavelet coefficient, i=1.
According to the embodiment, the wavelet hard threshold noise reduction processing of the general threshold is carried out on the high-frequency detail coefficients, so that a group of coefficients under the condition of approximate noise free is obtained, and interference caused by most external environments can be shielded.
In one embodiment, the step S40: based on the noise reduction coefficient, calculating an adaptive threshold, which specifically comprises the following steps:
s41, carrying out wavelet reconstruction on the coefficient after noise reduction to obtain a section of discrete sequence with the same length as the current signal, and marking the discrete sequence as
S42, taking the information entropy of the discrete sequence as an arc fault factor;
s43, calculating a load adaptation factor based on the coefficient reconstructed by the coefficient after noise reduction;
and S44, calculating the self-adaptive threshold according to the arc fault factor, the load adaptation factor and the empirical self-correction factor.
In one embodiment, the arc fault factor is formulated as:
wherein: the AFF is the arc fault factor and,represents the i +.>Values of the sequence, n D Representation->Number of coefficients in the sequence,/->Representing the offline sequence.
It should be noted that AFF meansThe average uncertainty level of the sequence is a characteristic quantity that has a certain correlation with arc faults.
In one embodiment, the load adaptation factor is formulated as:
wherein: LAF is the load adaptation factor, d i And (3) denoising the third layer detail coefficients of the current signal and reconstructing the coefficients, wherein i=1, 2, … and n.
In one embodiment, the adaptive threshold is formulated as:
thd＝k·AFF·LAF
wherein: thd is an adaptive threshold, AFF is an arc fault factor, LAF is a load adaptation factor, and k is an empirical self-correction factor.
The embodiment can combine the load adaptation factor and the fault arc factor to obtain fault distinguishing conditions which can adapt to different load conditions, and the detection of fault arc is prevented from being interfered by the load.
The characterization of AFF and LAF under resistive load is shown in fig. 6 and 7. The empirical self-correction factor k=10 given the reference -6 . Thus, the adaptive threshold can be calculated from the formula thd=k·aff·laf, the thd characterization under resistive load being shown in fig. 8.
In one embodiment, the step S50: fault arc detection based on the adaptive threshold, comprising the steps of:
s51, when a real-time threshold t of one detection period is larger than or equal to the self-adaptive threshold, determining that a series fault arc occurs;
and S52, when the real-time threshold t of one detection period is smaller than the self-adaptive threshold, determining that no series fault arc occurs.
It should be noted that, in this embodiment, the series fault arc (SAF) detection strategy is set based on the adaptive threshold, and when the t value of one detection period is equal to or greater than the given threshold thd, the SAF is set to 1, otherwise the SAF is set to 0. The detection strategy is formulated as:t i the t value representing the i-th detection period.
In addition, as shown in fig. 10, a second embodiment of the present invention proposes a fault arc detection system based on a coefficient of variation difference algorithm, the system comprising:
the acquisition module 10 is used for acquiring current signals of a line to be tested;
the feature extraction module 20 is configured to obtain a second order difference value sample sequence of the variation coefficient according to the current signal;
the decomposition noise reduction module 30 is configured to perform wavelet decomposition on the second-order differential value sample sequence, and perform noise reduction on the decomposition result to obtain a noise reduction coefficient;
a threshold calculation module 40, configured to calculate an adaptive threshold based on the noise reduction coefficient;
the detection module 50 is configured to perform fault arc detection based on the adaptive threshold.
The embodiment adopts the technical means of combining the variation coefficient difference algorithm, the self-adaptive threshold value, the wavelet decomposition reconstruction and the noise reduction, overcomes the problems of low accuracy, missing report and more false reports caused by the fact that the traditional algorithm cannot adapt to the threshold value in real time in fault detection, and further improves the reliability of detection.
In one embodiment, the feature extraction module 20 specifically includes:
a coefficient of variation extraction unit for extracting a coefficient of variation per half period of the current signal;
the first-order difference calculation unit is used for calculating a first-order difference value of the current half period based on the variation coefficient of the current half period and the variation coefficient of the next half period;
a second-order difference calculation unit for calculating a second-order difference value of the current half cycle based on the first-order difference value of the current half cycle and the first-order difference value of the next half cycle;
and the sample sequence construction unit is used for constructing the second-order differential value of each half period into the second-order differential value sample sequence.
In one embodiment, the decomposition noise reduction module 30 specifically includes:
the decomposition unit is used for carrying out wavelet decomposition on the second-order differential value sample sequence to obtain an approximation coefficient and a high-frequency detail coefficient;
and the noise reduction unit is used for carrying out hard threshold wavelet noise reduction processing on the high-frequency detail coefficient to obtain a noise-reduced coefficient.
In an embodiment, the noise reduction unit is specifically configured to perform the following steps:
when the absolute value of the high-frequency detail coefficient is larger than a given universal threshold lambda, the coefficient of the high-frequency detail coefficient is unchanged;
setting zero for the coefficient of the high-frequency detail coefficient when the absolute value of the high-frequency detail coefficient is smaller than or equal to a given universal threshold lambda;
wherein the universal threshold valueSigma is the standard deviation of the coefficients of the detail components, n D Is the number of coefficients of the detail component.
In one embodiment, the threshold calculation module 40 is specifically configured to:
the discrete sequence acquisition unit is used for carrying out wavelet reconstruction on the coefficient after noise reduction to obtain a section of discrete sequence with the same length as the current signal;
an arc fault factor calculation unit, configured to take the information entropy of the discrete sequence as an arc fault factor;
the load adaptation factor calculation unit is used for calculating a load adaptation factor based on the coefficient reconstructed by the coefficient after noise reduction;
and the adaptive threshold calculating unit is used for calculating the adaptive threshold according to the arc fault factor, the load adaptation factor and the empirical self-correction factor.
In one embodiment, the arc fault factor is formulated as:
wherein: the AFF is the arc fault factor and,represents the i +.>Values of the sequence, n D Representation->Number of coefficients in the sequence,/->Representing the offline sequence.
In one embodiment, the load adaptation factor is formulated as:
wherein: LAF is the load adaptation factor, d i And (3) denoising the third layer detail coefficients of the current signal and reconstructing the coefficients, wherein i=1, 2, … and n.
In one embodiment, the adaptive threshold is formulated as:
thd＝k·AFF·LAF
wherein: thd is an adaptive threshold, AFF is an arc fault factor, LAF is a load adaptation factor, and k is an empirical self-correction factor.
In one embodiment, the detection module 50 is specifically configured to perform the following steps:
when the t value of one detection period is larger than or equal to the self-adaptive threshold value, determining that a series fault arc occurs;
and when the t value of one detection period is smaller than the adaptive threshold value, determining that no series fault arc occurs.
In the embodiment, the coefficient of variation (C.V) of each half period and first-order and second-order differential values of the coefficient are extracted from a current signal to be detected as fault indication characteristics, and fault arc detection is carried out; obtaining an arc fault factor AFF, a load adaptation factor LAF and an empirical self-correction factor k through means of wavelet decomposition, noise reduction, reconstruction and the like, determining a real-time self-adaptation threshold thd, and then designing and applying an SAF detection strategy to a period to be detected; the problems of low accuracy, missing report and more false reports caused by the fact that the conventional algorithm cannot adapt to the threshold value in real time in fault detection are overcome, and the reliability of detection is further improved; the method can protect the life and property safety of the user and has main advantages in terms of improving detection efficiency, quick response and reducing calculation complexity compared with other detection methods.
It should be noted that, in other embodiments of the fault arc detection system based on the coefficient of variation difference algorithm or the implementation method thereof, reference may be made to the above embodiments of the method, and no redundancy is required here.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In the description of the present invention, the meaning of "plurality" means at least two, for example, two, three, etc., unless specifically defined otherwise.
While embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and not to be construed as limiting the invention, and that variations, modifications, alternatives and variations may be made to the above embodiments by one of ordinary skill in the art within the scope of the invention.
Claims (10)
1. The fault arc detection method based on the variation coefficient difference algorithm is characterized by comprising the following steps of:
collecting a current signal of a line to be tested;
obtaining a second-order difference value sample sequence of the variation coefficient according to the current signal;
performing wavelet decomposition on the second-order differential value sample sequence, and performing noise reduction treatment on the decomposition result to obtain a noise reduction coefficient;
calculating an adaptive threshold based on the post-noise reduction coefficient;
and detecting fault arc based on the adaptive threshold.
2. The fault arc detection method based on a coefficient of variation differential algorithm as claimed in claim 1, wherein the obtaining a sample sequence of a second order differential value of a coefficient of variation according to the current signal comprises:
extracting a variation coefficient of each half period of the current signal;
calculating a first-order differential value of the current half period based on the variation coefficient of the current half period and the variation coefficient of the next half period;
calculating a second-order differential value of the current half period based on the first-order differential value of the current half period and the first-order differential value of the next half period;
the second-order differential value of each half period is formed into the second-order differential value sample sequence.
3. The fault arc detection method based on a coefficient of variation difference algorithm as defined in claim 1, wherein the performing wavelet decomposition on the second-order difference value sample sequence and performing noise reduction processing on the decomposition result to obtain a noise-reduced coefficient comprises:
performing wavelet decomposition on the second-order differential value sample sequence to obtain an approximation coefficient and a high-frequency detail coefficient;
and carrying out hard threshold wavelet noise reduction processing on the high-frequency detail coefficient to obtain a noise-reduced coefficient.
4. The fault arc detection method based on a coefficient of variation difference algorithm as claimed in claim 3, wherein the performing hard threshold wavelet denoising processing on the high frequency detail coefficient to obtain a denoised coefficient comprises:
when the absolute value of the high-frequency detail coefficient is larger than a given universal threshold lambda, the coefficient of the high-frequency detail coefficient is unchanged;
setting zero for the coefficient of the high-frequency detail coefficient when the absolute value of the high-frequency detail coefficient is smaller than or equal to a given universal threshold lambda;
wherein the universal threshold valueSigma is a detail componentStandard deviation of coefficients, n D Is the number of coefficients of the detail component.
5. The fault arc detection method based on a coefficient of variation difference algorithm as claimed in claim 1, wherein the calculating an adaptive threshold based on the post-noise reduction coefficient comprises:
performing wavelet reconstruction on the coefficient after noise reduction to obtain a section of discrete sequence with the same length as the current signal;
taking the information entropy of the discrete sequence as an arc fault factor;
calculating a load adaptation factor based on the coefficient reconstructed from the noise-reduced coefficient;
and calculating the self-adaptive threshold according to the arc fault factor, the load adaptation factor and the empirical self-correction factor.
6. The arc fault detection method based on a coefficient of variation differential algorithm as claimed in claim 5, wherein the arc fault factor is formulated as:
wherein: the AFF is the arc fault factor and,represents the i +.>Values of the sequence, n D Representation->Number of coefficients in the sequence,/->Representing the offline sequence.
7. The fault arc detection method based on a coefficient of variation differential algorithm as in claim 5, wherein the load adaptation factor is formulated as:
wherein: LAF is the load adaptation factor, d i Coefficients reconstructed after noise reduction for the third layer detail coefficients of the current signal, i=1, 2.
8. The fault arc detection method based on a coefficient of variation difference algorithm as in claim 5, wherein the adaptive threshold is formulated as:
thd＝k·AFF·LAF
wherein: thd is an adaptive threshold, AFF is an arc fault factor, LAF is a load adaptation factor, and k is an empirical self-correction factor.
9. The fault arc detection method based on a coefficient of variation differential algorithm as claimed in claim 1, wherein the fault arc detection based on the adaptive threshold comprises:
when the real-time threshold t of one detection period is larger than or equal to the self-adaptive threshold, determining that a series fault arc occurs;
when the real-time threshold t of one detection period is smaller than the adaptive threshold, determining that no series fault arc occurs.
10. A fault arc detection system based on a coefficient of variation differential algorithm, the system comprising:
the acquisition module is used for acquiring current signals of the line to be detected;
the characteristic extraction module is used for obtaining a second-order difference value sample sequence of the variation coefficient according to the current signal;
the decomposition noise reduction module is used for carrying out wavelet decomposition on the second-order differential value sample sequence and carrying out noise reduction treatment on the decomposition result to obtain a noise reduction coefficient;
the threshold calculating module is used for calculating an adaptive threshold based on the noise reduction coefficient;
and the detection module is used for detecting fault arcs based on the self-adaptive threshold value.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310213243.2A CN117269660A (en) | 2023-03-02 | 2023-03-02 | Fault arc detection method and system based on variation coefficient difference algorithm |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310213243.2A CN117269660A (en) | 2023-03-02 | 2023-03-02 | Fault arc detection method and system based on variation coefficient difference algorithm |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117269660A true CN117269660A (en) | 2023-12-22 |
Family
ID=89203336
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310213243.2A Pending CN117269660A (en) | 2023-03-02 | 2023-03-02 | Fault arc detection method and system based on variation coefficient difference algorithm |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117269660A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117913828A (en) * | 2024-03-18 | 2024-04-19 | 国网山西省电力公司经济技术研究院 | Risk assessment method and system for power distribution system |
-
2023
- 2023-03-02 CN CN202310213243.2A patent/CN117269660A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117913828A (en) * | 2024-03-18 | 2024-04-19 | 国网山西省电力公司经济技术研究院 | Risk assessment method and system for power distribution system |
| CN117913828B (en) * | 2024-03-18 | 2024-07-05 | 国网山西省电力公司经济技术研究院 | Risk assessment method and system for power distribution system |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| JP5049675B2 (en) | Distribution line accident cause estimation system, method, and program | |
| US20110112779A1 (en) | Power quality meter and method of waveform anaylsis and compression | |
| CN103018629A (en) | Method for analyzing power system fault recording data based on Marla algorithm | |
| CN106597160B (en) | Electronic equipment fault detection method and device | |
| CN117390408B (en) | Power transformer operation fault detection method and system | |
| CN117269660A (en) | Fault arc detection method and system based on variation coefficient difference algorithm | |
| CN114397569A (en) | Circuit breaker fault arc detection method based on VMD parameter optimization and sample entropy | |
| Cai et al. | Multivariate detection of power system disturbances based on fourth order moment and singular value decomposition | |
| CN113391244A (en) | VMD-based transformer switching-on vibration signal characteristic frequency calculation method | |
| CN113671037B (en) | Post insulator vibration acoustic signal processing method | |
| KR20060040443A (en) | Noise reduction and quantity decision method in partial discharge measurement | |
| CN111780868A (en) | Transformer running state vibration and noise detection method and system by utilizing Jeffery difference | |
| CN117289087A (en) | Series fault arc detection method based on CZT conversion | |
| Ala et al. | A smart sensing method for real-time monitoring of low voltage series-arc-fault | |
| Yang et al. | A novel adaptive detection method for generic power quality waveform abnormality | |
| CN110286291B (en) | Method and system for detecting vibration and sound of running state of transformer by using principal components | |
| CN115828144A (en) | Signal sparse representation and fusion detection method, storage medium and electronic device | |
| CN108256750B (en) | Power equipment allocation method and system based on equipment relative service environment relevance | |
| Liu et al. | Power quality disturbance detection based on improved lifting Wavelet | |
| CN111649819A (en) | Transformer state vibration and sound detection signal filtering method and system using iteration soft threshold | |
| CN112307997B (en) | Power signal reconstruction method and system by using main mode decomposition | |
| CN112347922B (en) | Power signal filtering method and system by using Hankerl matrix | |
| CN110632477A (en) | Transformer running state vibration and sound detection method and system by using Hilbert space factor | |
| CN111505420B (en) | Online monitoring and diagnosing method and system for state of line arrester | |
| CN112327084B (en) | Method and system for detecting vibration and sound of running state of transformer by utilizing equidistant transformation |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |